gatesla commited on
Commit
292a35c
1 Parent(s): 9fceef7

Update app.py

Browse files

Trying to get the list working

Files changed (1) hide show
  1. app.py +11 -9
app.py CHANGED
@@ -66,12 +66,15 @@ def detect_objects(model_name,url_input,image_input,threshold):
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  elif 'yolos' in model_name:
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  model = YolosForObjectDetection.from_pretrained(model_name)
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-
 
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  if validators.url(url_input):
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  image = Image.open(requests.get(url_input, stream=True).raw)
 
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  elif image_input:
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  image = image_input
 
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  #Make prediction
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  processed_output_list = make_prediction(image, feature_extractor, model)
@@ -82,8 +85,8 @@ def detect_objects(model_name,url_input,image_input,threshold):
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  viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
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  # return [viz_img, processed_outputs]
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- print(type(viz_img))
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- return viz_img
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  def set_example_image(example: list) -> dict:
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  return gr.Image.update(value=example[0])
@@ -150,16 +153,15 @@ with demo:
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  img_but = gr.Button('Detect')
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- output_text1 = gr.Textbox(value="", label="Confidence Values URL")
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- output_text2 = gr.Textbox(value="", label="Confidence Values Upload")
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- # url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, output_text1],queue=True)
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- # img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, output_text2],queue=True)
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  # url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, _],queue=True)
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  # img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, _],queue=True)
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- url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
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- img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)
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  example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
 
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  elif 'yolos' in model_name:
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  model = YolosForObjectDetection.from_pretrained(model_name)
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+
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+ tb_label = ""
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  if validators.url(url_input):
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  image = Image.open(requests.get(url_input, stream=True).raw)
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+ tb_label = "Confidence Values URL"
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  elif image_input:
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  image = image_input
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+ tb_label = "Confidence Values Upload"
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  #Make prediction
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  processed_output_list = make_prediction(image, feature_extractor, model)
 
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  viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
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  # return [viz_img, processed_outputs]
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+ # print(type(viz_img))
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+ return viz_img, gr.Textbox(value=str(processed_outputs),label=tb_label)
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  def set_example_image(example: list) -> dict:
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  return gr.Image.update(value=example[0])
 
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  img_but = gr.Button('Detect')
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+ output_text1 = gr.Textbox(value="", label="Confidence Values")
 
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+ url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, output_text1],queue=True)
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+ img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, output_text1],queue=True)
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  # url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, _],queue=True)
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  # img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, _],queue=True)
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+ # url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
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+ # img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)
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  example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])